As organizations move toward event-driven architectures and real-time data processing, choosing the right streaming platform becomes critical. Apache Kafka and Apache Pulsar are two of the most popular open-source messaging systems, each offering powerful features for building scalable, reliable data pipelines.

In this post, we’ll compare Kafka and Pulsar across architecture, performance, scalability, and ecosystem to help you make an informed decision for your streaming workloads.

Kafka Overview

Apache Kafka is a distributed event streaming platform originally developed by LinkedIn. It is designed for high-throughput, fault-tolerant, and durable messaging.

Key features:

  • Append-only logs with partition-based storage
  • High-performance streaming via Kafka Streams
  • Strong ecosystem: Kafka Connect, ksqlDB, Confluent Platform
  • Persistent, replayable messaging model

Kafka is widely adopted in large-scale data processing, log aggregation, and analytics pipelines.

Pulsar Overview

Apache Pulsar is a cloud-native, distributed pub-sub system originally developed by Yahoo. It is designed with multi-tenancy, geo-replication, and streaming + queuing support built in.

Key features:

  • Decoupled storage and compute using Apache BookKeeper
  • True multi-tenant support with namespaces
  • Serverless functions (Pulsar Functions)
  • Topic compaction and dead-letter queues (DLQs)

Pulsar is gaining popularity for hybrid cloud use cases and event-driven microservices.

Architectural Comparison

Feature Kafka Pulsar
Storage Coupled with brokers (monolithic) Decoupled via BookKeeper
Multi-tenancy Limited Native (namespaces, isolation)
Geo-replication External tools (e.g., MirrorMaker) Built-in
Message model Publish-subscribe Pub-sub + queue
Serverless functions No Yes (Pulsar Functions)
Message retention Time/size-based Time/size + compaction

Pulsar’s architecture offers better isolation and flexibility, while Kafka’s model is more mature and simpler for many common use cases.

Performance and Scalability

  • Kafka scales by adding brokers and partitions. Performance can degrade with a very high number of partitions.
  • Pulsar scales more granularly with separate storage and serving layers, leading to better elasticity in some environments.

Pulsar excels in scenarios with many small topics or partitions, while Kafka performs well with larger batch processing and fewer partitions per topic.

Ecosystem and Tooling

Tooling/Feature Kafka Pulsar
Stream processing Kafka Streams, ksqlDB Pulsar Functions, Flink, Spark
Connectors Kafka Connect (large ecosystem) Pulsar IO (growing library)
Monitoring JMX, Confluent Control Center Prometheus, Pulsar Dashboard
Schema registry Confluent Schema Registry Built-in
Language support Java, Python, Go, Node.js, .NET Java, Python, Go, C++, Node.js

Kafka has a more mature and extensive ecosystem, especially for stream processing. Pulsar’s multi-language support and serverless compute make it attractive for microservice-based systems.

When to Use Kafka

Choose Apache Kafka when:

  • You need high-throughput and simple event streaming
  • You’re already using the Confluent ecosystem
  • Your team is experienced with Kafka and its APIs
  • You want stream processing capabilities (Kafka Streams, ksqlDB)

When to Use Pulsar

Choose Apache Pulsar when:

  • You need multi-tenancy, geo-replication, or multi-region architecture
  • You need both queue and stream semantics
  • You prefer decoupled storage and compute
  • You want to build serverless apps with Pulsar Functions

Summary Comparison

Criteria Kafka Pulsar
Maturity High (widespread adoption) Medium (fast-growing)
Storage model Broker-managed logs BookKeeper-managed ledgers
Real-time processing Kafka Streams / ksqlDB Pulsar Functions / Flink
Deployment complexity Simpler More components (BookKeeper, etc.)
Cloud-native features Limited Native
Ecosystem Rich and mature Evolving

Conclusion

Both Apache Kafka and Apache Pulsar are excellent choices for real-time data streaming, but their strengths vary depending on the use case.

  • Choose Kafka for mature pipelines, large-scale stream processing, and when ecosystem integration is key.
  • Choose Pulsar when you need multi-tenancy, geo-distribution, or event queueing alongside streaming.

Ultimately, the best choice depends on your architectural goals, scalability needs, and operational constraints.